Designing and Modeling of a Dual-Band Rectenna With Compact Dielectric Resonator Antenna
Bibliographic record
Abstract
In this letter, a compact dual-band rectenna is modeled and developed for low radio frequency (RF) power harvesting. The proposed theoretical model can provide a comprehensive analysis of the rectenna, including the power conversion efficiency (PCE) of the diode, matching efficiency of the rectifier, and total PCE of the rectenna. In the rectenna design, a very compact omnidirectional dielectric resonator antenna (DRA) is proposed with a broad bandwidth exceeding 40%. Meanwhile, a dual-band rectifier is designed based on a single branch in the low-power range with bandwidths covering the two fifth-generation (5G) frequency bands in China (2.515–2.675 and 3.4–3.6 GHz). It was found that the theoretical model can show a high accuracy with the calculation error within 5% in analyzing the dual-band rectifier. The DRA and the dual-band rectifier are integrated to form a compact dual-band rectenna. It shows a higher PCE than those of the existing multiband designs working at similar low-power levels. It is hoped to be implemented in 5G-enabled Internet of Things (IoT) applications for powering wireless sensor nodes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".